




"""simple docstring"""




import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


a_        =       datasets.logging.get_logger(__name__)


a_        =       '\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

a_        =       '\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

a_        =       '\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

a_        =       {
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,					_KWARGS_DESCRIPTION		)
class   UpperCAmelCase_    (			datasets.Metric		):
		def 	_lowerCamelCase							(						self       )						->       List[str]:
					return datasets.MetricInfo(
					    description=_DESCRIPTION				,						citation=_CITATION				,						homepage='''https://github.com/google-research/bleurt'''				,						inputs_description=_KWARGS_DESCRIPTION				,						features=datasets.Features(
					        {
					            '''predictions''': datasets.Value('''string'''				,						id='''sequence'''       ),
					            '''references''': datasets.Value('''string'''				,						id='''sequence'''       ),
					        }       )				,						codebase_urls=['''https://github.com/google-research/bleurt''']				,						reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']				,						)
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_       )						->       Tuple:
					# check that config name specifies a valid BLEURT model
					if self.config_name == "default":
								logger.warning(
								    '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
								    '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''       )
								__lowercase			:  Union[str, Any]								=       '''bleurt-base-128'''

					if self.config_name.lower() in CHECKPOINT_URLS:
								__lowercase			:  List[str]								=       self.config_name.lower()

					elif self.config_name.upper() in CHECKPOINT_URLS:
								__lowercase			:  int								=       self.config_name.upper()

					else:
								raise KeyError(
								    F"""{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"""       )

					# download the model checkpoint specified by self.config_name and set up the scorer
					__lowercase			:  str								=       dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]       )
					__lowercase			:  Optional[int]								=       score.BleurtScorer(os.path.join(UpperCamelCase_				,						UpperCamelCase_       )       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_       )						->       int:
					__lowercase			:  List[Any]								=       self.scorer.score(references=UpperCamelCase_				,						candidates=UpperCamelCase_       )
					return {"scores": scores}





"""simple docstring"""






import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}



class 			lowercase					( lowercase__		):
			lowercase        =     '''segformer'''






			def __init__(self  :	str      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=3      ,SCREAMING_SNAKE_CASE_  :	List[str]=4      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=[2, 2, 2, 2]      ,SCREAMING_SNAKE_CASE_  :	List[str]=[8, 4, 2, 1]      ,SCREAMING_SNAKE_CASE_  :	int=[32, 64, 160, 256]      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=[7, 3, 3, 3]      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=[4, 2, 2, 2]      ,SCREAMING_SNAKE_CASE_  :	List[Any]=[1, 2, 5, 8]      ,SCREAMING_SNAKE_CASE_  :	Dict=[4, 4, 4, 4]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]="gelu"      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=0.0      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=0.0      ,SCREAMING_SNAKE_CASE_  :	List[str]=0.1      ,SCREAMING_SNAKE_CASE_  :	List[Any]=0.02      ,SCREAMING_SNAKE_CASE_  :	str=0.1      ,SCREAMING_SNAKE_CASE_  :	Dict=1e-6      ,SCREAMING_SNAKE_CASE_  :	List[str]=256      ,SCREAMING_SNAKE_CASE_  :	List[str]=255      ,**SCREAMING_SNAKE_CASE_  :	Tuple      ,)       ->	int:





				"""simple docstring"""


				super().__init__(**SCREAMING_SNAKE_CASE_			)

				if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
					warnings.warn(
					    '''Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'''
					    ''' removed, as the behaviour will default to that of reshape_last_stage = True.'''      ,SCREAMING_SNAKE_CASE_      ,)

				lowerCAmelCase												=  num_channels
				lowerCAmelCase												=  num_encoder_blocks
				lowerCAmelCase												=  depths
				lowerCAmelCase												=  sr_ratios
				lowerCAmelCase												=  hidden_sizes
				lowerCAmelCase												=  patch_sizes
				lowerCAmelCase												=  strides
				lowerCAmelCase												=  mlp_ratios
				lowerCAmelCase												=  num_attention_heads
				lowerCAmelCase												=  hidden_act
				lowerCAmelCase												=  hidden_dropout_prob
				lowerCAmelCase												=  attention_probs_dropout_prob
				lowerCAmelCase												=  classifier_dropout_prob
				lowerCAmelCase												=  initializer_range
				lowerCAmelCase												=  drop_path_rate
				lowerCAmelCase												=  layer_norm_eps
				lowerCAmelCase												=  decoder_hidden_size
				lowerCAmelCase												=  kwargs.get('''reshape_last_stage'''      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  semantic_loss_ignore_index





class 			lowercase					( lowercase__		):
			lowercase        =     version.parse('''1.11'''		)






			@property
			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Mapping[str, Mapping[int, str]]:





				"""simple docstring"""


				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]			)






			@property
			def 					UpperCAmelCase							(self  :	str			)       ->	float:





				"""simple docstring"""


				return 1e-4






			@property
			def 					UpperCAmelCase							(self  :	Dict			)       ->	int:





				"""simple docstring"""


				return 12


